Chapter 12 - Hybrid artificial intelligence models for predicting daily runoff

Accurate estimation of reference evapotranspiration (ETo) is profoundly crucial in crop modeling, sustainable management, hydrological water simulation, and irrigation scheduling, since it accounts for more than two-thirds of global precipitation losses. Therefore, ETo-based estimation is a major concern in the hydrological cycle. The estimation of ETo can be determined using various methods, including field measurement (the scale of the lysimeter), experimental methods, and mathematical equations. The Food and Agriculture Organization recommended the Penman-Monteith (FAO-56 PM) method which was identified as the standard method of ETo estimation. However, this equation requires a large number of measured climatic data (maximum and minimum air temperature, relative humidity, solar radiation, and wind speed) that are not always available on meteorological stations. Over the decade, the artificial intelligence (AI) models have received more attention for estimating ETo on multi-time scales. This research explores the potential of new hybrid AI model, i.e., support vector regression (SVR) integrated with grey wolf optimizer (SVR-GWO) for estimating monthly ETo at Algiers, Tlemcen, and Annaba stations located in the north of Algeria. Five climatic variables namely relative humidity (RH), maximum and minimum air temperatures (Tmax and Tmin), solar radiation (Rs), and wind speed (Us) were used for model construction and evaluation. The proposed hybrid SVR-GWO model was compared against hybrid SVR-genetic algorithm (SVR-GA), SVR-particle swarm optimizer (SVR-PSO), conventional artificial neural network (ANN), and empirical (Turc, Ritchie, Thornthwaite, and three versions of Valiantzas methods) models by using root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), Pearson correlation coefficient (PCC), and Willmott index (WI), and through graphical interpretation. Through the results obtained, the performance of the SVR-GWO provides very promising and occasionally competitive results compared to other data-driven and empirical methods at study stations. Thus, the proposed SVR-GWO model with five climatic input variables outperformed the other models (RMSE = 0.0776/0.0613/0.0374mm, NSE = 0.9953/ 0.9990/0.9995, PCC = 0.9978/0.9995/0.9998 and WI = 0.9988/0.9997/0.9999) for estimating ETo at Algiers, Tlemcen, and Annaba stations, respectively. In conclusion, the results of this research indicate the suitability of the proposed hybrid artificial intelligence model (SVR-GWO) at the study stations. Besides, promising results encourage researchers to transfer and test these models in other locations in the world in future works.

The growing reliance on renewable energy, particularly solar power, presents significant challenges to grid stability due to the variability and intermittency of energy generation. Hybrid Artificial Intelligence (AI) models have emerged as a transformative solution, integrating multiple AI techniques to address these challenges effectively. This study systematically reviewed 130 peer-reviewed articles, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure a transparent and rigorous process. The review highlights how hybrid AI models, by combining neural networks, reinforcement learning, evolutionary algorithms, and advanced architectures like transformers, achieve superior performance in predictive modeling, fault detection, load balancing, and scalability. Predictive modeling for solar power variability saw up to a 30% improvement in mean absolute error (MAE), while fault detection systems exhibited enhanced precision and recall, diagnosing multiple simultaneous faults in real time. Hybrid AI models also excel in load balancing and demand forecasting, reducing energy wastage by 20% and enabling dynamic resource allocation. Furthermore, their scalability and adaptability make them ideal for large-scale, distributed energy networks, addressing the complexities of modern smart grids. Despite these advancements, persistent challenges such as real-time data integration, standardization, and deployment barriers remain. The study underscores the need for future research to address these limitations through innovations like federated learning and decentralized AI frameworks. This comprehensive review demonstrates the critical role of hybrid AI in advancing grid stability, optimizing renewable energy integration, and paving the way for sustainable, resilient energy infrastructures.

Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research directions

Prediction of sediment heavy metal at the Australian Bays using newly developed hybrid artificial intelligence models.

The coronavirus disease 2019 (COVID-19) breaking out in late December 2019 is gradually being controlled in China, but it is still spreading rapidly in many other countries and regions worldwide. It is urgent to conduct prediction research on the development and spread of the epidemic. In this article, a hybrid artificial-intelligence (AI) model is proposed for COVID-19 prediction. First, as traditional epidemic models treat all individuals with coronavirus as having the same infection rate, an improved susceptible-infected (ISI) model is proposed to estimate the variety of the infection rates for analyzing the transmission laws and development trend. Second, considering the effects of prevention and control measures and the increase of the public's prevention awareness, the natural language processing (NLP) module and the long short-term memory (LSTM) network are embedded into the ISI model to build the hybrid AI model for COVID-19 prediction. The experimental results on the epidemic data of several typical provinces and cities in China show that individuals with coronavirus have a higher infection rate within the third to eighth days after they were infected, which is more in line with the actual transmission laws of the epidemic. Moreover, compared with the traditional epidemic models, the proposed hybrid AI model can significantly reduce the errors of the prediction results and obtain the mean absolute percentage errors (MAPEs) with 0.52%, 0.38%, 0.05%, and 0.86% for the next six days in Wuhan, Beijing, Shanghai, and countrywide, respectively.

Study on prediction of ocean effective wave height based on hybrid artificial intelligence model

Predicting uniaxial compressive strength of oil palm shell concrete using a hybrid artificial intelligence model

For effective planning of irrigation scheduling, water budgeting, crop simulation, and water resources management, the accurate estimation of reference evapotranspiration (ETo) is essential. In the current study, the hybrid support vector regression (SVR) coupled with Whale Optimization Algorithm (SVR-WOA) was employed to estimate the monthly ETo at Algiers and Tlemcen meteorological stations positioned in the north of Algeria under three different optimal input scenarios. Monthly climatic parameters, i.e., solar radiation (Rs), wind speed (Us), relative humidity (RH), and maximum and minimum air temperatures (Tmax and Tmin) of 14years (2000-2013), were obtained from both stations. The accuracy of the hybrid SVR-WOA model was appraised against hybrid SVR-MVO (Multi-Verse Optimizer), and SVR-ALO (Ant Lion Optimizer) models through performance measures, i.e., mean absolute error (MAE), root-mean-square error (RMSE), index of scattering (IOS), index of agreement (IOA), Pearson correlation coefficient (PCC), Nash-Sutcliffe efficiency (NSE), and graphical interpretation (time-variation and scatter plots, radar chart, and Taylor diagram). The results showed that the SVR-WOA model performed superior to the SVR-MVO and SVR-ALO models at both stations in all scenarios. The SVR-WOA-1 model with five inputs (i.e., Tmin, Tmax, RH, Us, Rs: scenario-1) had the lowest value of MAE = 0.0658/0.0489mm/month, RMSE = 0.0808/0.0617mm/month, IOS = 0.0259/0.0165, and the highest value of NSE = 0.9949/0.9989, PCC = 0.9975/0.9995, and IOA = 0.9987/0.9997 for testing period at both stations, respectively. The proposed hybrid SVR-WOA model was found to be more appropriate and efficient in comparison to SVR-MVO and SVR-ALO models for estimating monthly ETo in the study region.

Monitoring and prediction of drought using standardized metrics of rainfall are of great importance for sustainable planning and management of water resources on regional and global scales. In this research, heuristic approaches including co-active neuro fuzzy inference system (CANFIS), multi-layer perceptron neural network (MLPNN), and multiple linear regression (MLR) were used for prediction of meteorological drought based on Effective Drought Index (EDI) at 13 stations located in Uttarakhand State, India. The EDI was calculated using monthly rainfall time-series data, and the significant input variables (lags) for CANFIS, MLPNN, and MLR models were derived using autocorrelation and partial autocorrelation functions (ACF and PACF) at 5% significance level. The predicted values of EDI obtained by CANFIS, MLPNN, and MLR models were compared with the calculated values based on root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), coefficient of correlation (COC), and Willmott index (WI). The visual interpretation was also made using line diagram, scatter plot, and Taylor diagram (TD). The evaluation of results revealed that the CANFIS and MLPNN models outperformed than the MLR models for meteorological drought prediction at study stations. Also, the results of this research can be utilized for the decision-making of remedial schemes to cope with meteorological drought in the study region.

Recent developments on shear strength (Vf) of steel fiber-reinforced concrete beam (SFRCB) simulation have been shifted to the implementation of the computer aid advancements. The current study is attempted to explore new hybrid artificial intelligence (AI) model called integrative support vector regression with firefly optimization algorithm (SVR-FFA) for shear strength prediction of SFRCB. The developed hybrid predictive model is constructed using laboratory experimental data set gathered from the literature and belongs to the shear failure capacity. The related beam dimensional and concrete properties are utilized as input attributes to predict Vf. The proposed SVR-FFA model is validated against classical SVR model and eight empirical formulations obtained from published researches. The attained results of the proposed hybrid AI model exhibited a reliable resultant performance in terms of prediction accuracy. Based on the examined root-mean-square error (RMSE) and the correlation coefficient (R2) over the testing phase, SVR-FFA achieved (RMSE ≈ 0.25 MPa) and (R2 ≈ 0.96).

Foamed concrete material is a sustainable material which is widely used in the construction industry due to their sustainability. Accurate prediction of their compressive strength is vital for structural design. However, empirical methods are limited to consider simultaneously all influencing factors in predicting the compressive strength of foamed concrete materials. Thus, this study proposed a novel hybrid artificial intelligence (AI) model which couples the least squares support vector regression (LSSVR) with the grey wolf optimization (GWO) to consider effectively the influencing factors and improve the predictive accuracy in predicting the foamed concrete’s compressive strength. Performance of the proposed model was evaluated using a real-world dataset. Comparison results confirm that the proposed GWO–LSSVR model was superior than the support vector regression, artificial neural networks, random forest, and M5Rules with the improvement rate of 144.2–284.0% in mean absolute percentage error (MAPE). Notably, the evaluation results show that the GWO–LSSVR model showed the good agreement between the actual and predicted values with the correlation coefficient of 0.991 and MAPE of 3.54%. Thus, the proposed AI model was suggested as an effective tool for designing foamed concrete materials.

An enhanced hybrid artificial intelligence model was developed for soil temperature (ST) prediction. Among several soil characteristics, soil temperature is one of the essential elements impacting the biological, physical and chemical processes of the terrestrial ecosystem. Reliable ST prediction is significant for multiple geo-science and agricultural applications. The proposed model is a hybridization of adaptive neuro-fuzzy inference system with optimization methods using mutation Salp Swarm Algorithm and Grasshopper Optimization Algorithm (ANFIS-mSG). Daily weather and soil temperature data for nine years (1 of January 2010 - 31 of December 2018) from five meteorological stations (i.e., Baker, Beach, Cando, Crary and Fingal) in North Dakota, USA, were used for modeling. For validation, the proposed ANFIS-mSG model was compared with seven models, including classical ANFIS, hybridized ANFIS model with grasshopper optimization algorithm (ANFIS-GOA), salp swarm algorithm (ANFIS-SSA), grey wolf optimizer (ANFIS-GWO), particle swarm optimization (ANFIS-PSO), genetic algorithm (ANFIS-GA), and Dragonfly Algorithm (ANFIS-DA). The ST prediction was conducted based on maximum, mean and minimum air temperature (AT). The modeling results evidenced the capability of optimization algorithms for building ANFIS models for simulating soil temperature. Based on the statistical evaluation; for instance, the root mean square error (RMSE) was reduced by 73%, 74.4%, 71.2%, 76.7% and 80.7% for Baker, Beach, Cando, Crary and Fingal meteorological stations, respectively, throughout the testing phase when ANFIS-mSG was used over the standalone ANFIS models. In conclusion, the ANFIS-mSG model was demonstrated as an effective and simple hybrid artificial intelligence model for predicting soil temperature based on univariate air temperature scenario.

Improving artificial intelligence models accuracy for monthly streamflow forecasting using grey Wolf optimization (GWO) algorithm

Multi-step daily forecasting of reference evapotranspiration for different climates of India: A modern multivariate complementary technique reinforced with ridge regression feature selection

Chapter 23 - Support vector regression model optimized with GWO versus GA algorithms: Estimating daily pan-evaporation